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Channel update Based on your requests, we launched: 🧠 Programming Quizzes 📚 Free Programming Books The books channel was our most popular one before, but it was removed due to copyright issues. Because of the huge interest, we decided to bring it back, sharing free and open books. You also requested hands-on project based learning. We are working on it! 👨‍💻 Thanks for the support. More coming soon 🚀

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13:45

05.02.2026

✅ Top DSA Interview Questions with Answers: Part-4 📘⚙️ 4️⃣1️⃣ What is Longest Common Subsequence (LCS)? LCS is the longest sequence that appears in the same order in both strings but not necessarily contiguously. Used in: diff tools, DNA sequencing. Approach: Dynamic Programming, O(n × m) 4️⃣2️⃣ What is Longest Increasing Subsequence (LIS)? It is the longest subsequence where each element is greater than the previous one. Approach: - DP: O(n²) - Binary Search: O(n log n) 4️⃣3️⃣ What is a Palindrome Substring problem? Find the longest or total number of substrings that are palindromes. Approach: - Expand Around Center (O(n²)) - DP Table - Manacher’s Algorithm (O(n)) 4️⃣4️⃣ Difference between Greedy and Dynamic Programming? - Greedy: Makes the best local choice, may miss optimal global solution. - DP: Explores all choices with memoization, guarantees optimality. Example: - Greedy: Coin change (not always optimal) - DP: Coin change (optimal) 4️⃣5️⃣ What is Big-O Notation? It expresses time/space complexity in terms of input size n. Examples: - O(1): Constant - O(n): Linear - O(n²): Quadratic - O(log n): Binary Search 4️⃣6️⃣ Time vs Space Complexity? - Time Complexity: Time taken vs input size - Space Complexity: Memory used Goal: Optimize both without sacrificing correctness. 4️⃣7️⃣ How to find time complexity of recursive function? Use Recurrence Relation Example:

T(n) = T(n/2) + O(1) → O(log n)  
T(n) = 2T(n/2) + O(n) → O(n log n)

4️⃣8️⃣ What are Amortized Time Complexities? It’s the average time per operation over a sequence of operations. Example: Dynamic array resizing: - Most inserts: O(1) - Occasional resize: O(n) - Amortized: O(1) 4️⃣9️⃣ What is Tail Recursion? Recursive call is the last operation in the function. Benefit: Optimized by compilers to reduce stack usage. Example:

    
        def factorial(n, acc=1):
    if n == 0:
        return acc
    return factorial(n-1, acc*n){}

5️⃣0️⃣ How to solve coding questions in interviews? - Understand the problem - Ask clarifying questions - Think out loud - Start with brute force - Optimize step-by-step - Test edge cases - Use clean, modular code 💬 Tap ❤️ for more!

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12:31

05.02.2026

✅ Top DSA Interview Questions with Answers: Part-3 🧠 21. What is the Sliding Window technique? It’s an optimization method used to reduce time complexity in problems involving arrays or strings. You create a "window" over a subset of data and slide it as needed, updating results on the go. Example use case: Find the maximum sum of any k consecutive elements in an array. 22. Explain the Two-Pointer technique. This involves using two indices (pointers) to traverse a data structure, usually from opposite ends or the same direction. It's helpful for searching pairs or reversing sequences efficiently. Common problems: Two-sum, palindrome check, sorted array partitioning. 23. What is the Binary Search algorithm? It’s an efficient algorithm to find an element in a sorted array by repeatedly dividing the search range in half. Time Complexity: O(log n) Key idea: Compare the target with the middle element and eliminate half the array each step. 24. What is the Merge Sort algorithm? A divide-and-conquer sorting algorithm that splits the array into halves, sorts them recursively, and then merges them. Time Complexity: O(n log n) Stable? Yes Extra space? Yes, due to merging. 25. What is the Quick Sort algorithm? It chooses a pivot, partitions the array so elements < pivot are left, and > pivot are right, then recursively sorts both sides. Time Complexity: Avg – O(n log n), Worst – O(n²) Fast in practice, but not stable. 26. Difference between Merge Sort and Quick Sort - Merge Sort is stable, consistent in performance (O(n log n)), but uses extra space. - Quick Sort is faster in practice and works in-place, but may degrade to O(n²) if pivot is poorly chosen. 27. What is Insertion Sort and how does it work? It builds the sorted list one item at a time by comparing and inserting items into their correct position. Time Complexity: O(n²) Best Case (nearly sorted): O(n) Stable? Yes Space: O(1) 28. What is Selection Sort? It finds the smallest element from the unsorted part and swaps it with the beginning. Time Complexity: O(n²) Space: O(1) Stable? No Rarely used due to inefficiency. 29. What is Bubble Sort and its drawbacks? It repeatedly compares and swaps adjacent elements if out of order. Time Complexity: O(n²) Space: O(1) Drawback: Extremely slow for large data. Educational, not practical. 30. What is the time and space complexity of common sorting algorithms? - Bubble Sort → Time: O(n²), Space: O(1), Stable: Yes - Selection Sort → Time: O(n²), Space: O(1), Stable: No - Insertion Sort → Time: O(n²), Space: O(1), Stable: Yes - Merge Sort → Time: O(n log n), Space: O(n), Stable: Yes - Quick Sort → Avg Time: O(n log n), Worst: O(n²), Space: O(log n), Stable: No Double Tap ♥️ For Part-4

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10:20

04.02.2026

✅ Top DSA Interview Questions with Answers: Part-2 🧠 11. What is the difference between BFS and DFS? - BFS (Breadth-First Search): Explores neighbors first (level by level). Uses a queue. - DFS (Depth-First Search): Explores depth (child nodes) first. Uses a stack or recursion. Used in graph/tree traversals, pathfinding, cycle detection. 12. What is a Heap? A binary tree with heap properties: - Max-Heap: Parent ≥ children - Min-Heap: Parent ≤ children Used in priority queues, heap sort, scheduling algorithms. 13. What is a Trie? A tree-like data structure used to store strings. Each node represents a character. Used in: autocomplete, spell-checkers, prefix search. 14. What is a Graph? A graph is a collection of nodes (vertices) and edges. - Can be directed/undirected, weighted/unweighted. Used in: networks, maps, recommendation systems. 15. Difference between Directed and Undirected Graph? - Directed: Edges have direction (A → B ≠ B → A) - Undirected: Edges are bidirectional (A — B) Used differently based on relationships (e.g., social networks vs. web links). 16. What is the time complexity of common operations in arrays and linked lists? - Array: - Access: O(1) - Insert/Delete: O(n) - Linked List: - Access: O(n) - Insert/Delete: O(1) at head 17. What is recursion? When a function calls itself to solve a smaller subproblem. Requires a base case to stop infinite calls. Used in: tree traversals, backtracking, divide & conquer. 18. What are base case and recursive case? - Base Case: Condition that ends recursion - Recursive Case: Part where the function calls itself Example:

    
        def fact(n):
    if n == 0: return 1   # base case
    return n * fact(n-1)  # recursive case{}

19. What is dynamic programming? An optimization technique that solves problems by breaking them into overlapping subproblems and storing their results (memoization). Used in: Fibonacci, knapsack, LCS. 20. Difference between Memoization and Tabulation? - Memoization (Top-down): Uses recursion + caching - Tabulation (Bottom-up): Uses iteration + table Both store solutions to avoid redundant calculations. Double Tap ♥️ For Part-3

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